Data Visualization 101: How to Present Raw Data in a Powerful & Comprehensible Way
Data visualization is the process of presenting data that leads to actionable insights, in a way that is easy to understand.
Data visualization helps people to see patterns and trends in data. It can be used for many purposes, such as to tell stories, answer questions, and make comparisons. It can also be used to help people make decisions for business or personal use.
Data visualization is a powerful tool because it allows us to see what we might not otherwise notice or know about.
Why Do We Need Data Visualization?
Data visualization is the process of converting complex information into a graphic form. It is a way of representing data in such a way that it can be easily seen and understood by the human eye.
The most common use cases for data visualization are to make complex data easier to understand and to make it possible for people who are not trained in statistics or other fields of study, to get insights from the data.
Data visualization is used in many fields, including business intelligence, science, engineering, medicine, and of course, marketing.
How to Get Started With The Best Tools For Designing Your Own Charts And Graphs
Creating charts and graphs is not an easy task. There are many tools available to help you create charts and graphs.
Some of the best tools for creating charts and graphs are:
Excel: Excel is the most widely used spreadsheet program with built-in charting, line graphs, scatter plots, and other visualizations. It is relatively easy to use and is a good choice for simple data visualization tasks.
Tableau: Tableau is a data visualization tool that is specifically designed for creating interactive charts, maps, and dashboards. It has a wide range of visualization options and is popular in business and data analytics.
Google Charts: Google Charts is a free, web-based data visualization tool that is part of the Google Ecosystem, allowing for integrations with tools like Data Studio and Sheets.
D3.js: D3.js is a JavaScript library that is optimized for creating interactive data visualizations. The library is highly customizable and is a solid choice for more complex data visualization projects.
Matplotlib: Matplotlib is an incredibly powerful and popular data visualization library for Python that is widely used in the scientific computing industry. Its vast range of customizable options make it appealing to developers looking to create custom visualizations as well as researchers who need to generate high-precision figures.
ggplot2: ggplot2 is a powerful and comprehensive data visualization package for the R programming language that allows users to create stunning visualizations of their data quickly and easily. It utilizes a unique grammar-based system to make creating graphs, charts, and maps an effortless process that can be used by both experts and beginners alike.
Plotly: Plotly is an advanced cloud-based data visualization solution that has a huge library of chart types and allows you to create highly interactive charts and dashboards with stunning visuals and animations.
Seaborn: Seaborn is another data visualization library for Python that provides a high-level interface for displaying attractive and informative statistical graphics.
6 Essential Types of Data Visualizations Every Marketer Needs To Know
Data visualization is an important part of marketing and it helps to tell a story. It helps marketers to understand their customers and find the best way to reach them. It also allows them to present their findings to stakeholders in an easily digestible format.
There are six essential types of data visuals that every marketer needs to know about: Pie charts, bar charts, line graphs, scatter plots, area charts, and bubble charts. Each type has its own strengths and weaknesses and can be used to effectively communicate different messages.
Visuals are a powerful tool for marketing. Charts and graphs can help convey complex information quickly and clearly, eliminating guesswork and providing research to decision makers.
